Abstract

Extreme precipitation events with high local precipitation intensities, heavy snowfall or extensive freezing rain can have devastating impacts on society and economy. Not only is the quantitative forecast of such events sometimes difficult and associated with large uncertainties, also are the potential consequences highly complex and challenging to predict. It is thus a demanding task to anticipate or nowcast the impacts of extreme precipitation, even more so in situations where human lives or critical infrastructure might be at risk.
In recent years, the term “cascading effects” has been increasingly used to describe events in which an initial trigger leads to a sequence of consequences with significant magnitude. We here analyze three examples for different precipitation types where the initial triggering event generated a cascade of events and impacts, namely a convective precipitation event in the Swiss Prealps, a freezing rain in Slovenia, and a heavy snowfall episode in Catalonia. With the aim to improve process understanding of complex precipitation-triggered events, we assess the prediction of the selected events and analyze the cascading effects that caused diverse impacts. To this end, we use a framework of cascading effects which should ultimately allow the development of a better design risk assessment and management strategies.
Our findings confirm that damage of extreme precipitation events is clearly related to the knowledge of potential cascading effects. Major challenges of predicting cascading effects are the high complexity, the interdependencies and the increasing uncertainty along the cascade. We propose a framework for cascading effects including two approaches: (i) one to analyze cascading effects during past extreme precipitation events, which then serves as a basis for a (ii) more generalized approach to increase the preparedness level of operational services before and during future extreme precipitation events and to anticipate potential cascading effects of extreme precipitation. Both approaches are based on pathway schemes that can be used in addition to numerical models or hazard maps to analyze and predict potential cascading effects, but also as training tools.